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ISLR02.R
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ISLR02.R
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# An Introduction to Statistical Learning with Applications in R
# by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
# Chapter 2: Statistical Learning
# 2.3 Lab: Introduction to R
# 2.3.1 Basic Commands
x <- c(1, 3, 2, 5)
x
x = c(1, 6, 2)
x
y = c(1, 4, 3)
length(x)
length(y)
x + y
ls()
rm(x, y)
ls()
rm(list = ls())
?matrix
x <- matrix(data = c(1,2,3,4), nrow = 2, ncol = 2)
x
x <- matrix(c(1, 2, 3, 4), 2, 2)
matrix(c(1, 2, 3, 4), 2, 2, byrow = TRUE)
sqrt(x)
x^2
x <- rnorm(50)
y <- x + rnorm(50, mean = 50, sd = 0.1)
cor(x, y)
set.seed(1303)
rnorm(50)
set.seed(3)
y <- rnorm(100)
mean(y)
var(y)
sqrt(var(y))
sd(y)
# 2.3.2 Graphics
x <- rnorm(100)
y <- rnorm(100)
plot(x, y)
plot(x, y,
xlab = "this is the x-axis", ylab = "this is the y-axis",
main = "Plot of X vs Y")
pdf("Figure.pdf")
plot(x, y, col = "green")
dev.off()
x <- seq(1, 10)
x
x <- 1:10
x
x <- seq(-pi, pi, length = 50)
y <- x
f <- outer(x, y, function(x, y) cos(y)/(1 + x^2))
contour(x, y, f)
contour(x, y, f, nlevels = 45, add = T)
fa <- (f - t(f))/2
contour(x, y, fa, nlevels = 15)
image(x, y, fa)
persp(x, y, fa)
persp(x, y, fa, theta = 30)
persp(x, y, fa, theta = 30, phi = 20)
persp(x, y, fa, theta = 30, phi = 70)
persp(x, y, fa, theta = 30, phi = 40)
# 2.3.3 Indexing Data
A <- matrix(1:16,4,4)
A
A[2,3]
A[c(1, 3), c(2, 4)]
A[1:3, 2:4]
A[1:2, ]
A[ , 1:2]
A[1, ]
A[-c(1,3 ), ]
A[-c(1, 3), -c(1, 3, 4)]
dim(A)
# 2.3.4 Loading Data
Auto <- read.table("Auto.data")
fix(Auto)
Auto <- read.table("Auto.data", header = T, na.strings = "?")
fix(Auto)
Auto <- read.csv("Auto.csv", header = T, na.strings = "?")
fix(Auto)
dim(Auto)
Auto[1:4, ]
Auto <- na.omit(Auto)
dim(Auto)
names(Auto)
# 2.3.5 Additional Graphical and Numerical Summaries
plot(cylinders, mpg)
plot(Auto$cylinders, Auto$mpg)
attach(Auto)
plot(cylinders, mpg)
cylinders <- as.factor(cylinders)
plot(cylinders, mpg)
plot(cylinders, mpg, col="red")
plot(cylinders, mpg, col="red", varwidth = T)
plot(cylinders, mpg, col="red", varwidth = T, horizontal = T)
plot(cylinders, mpg, col="red", varwidth = T, xlab = "cylinders", ylab = "MPG")
hist(mpg)
hist(mpg, col = 2)
hist(mpg, col = 2, breaks = 15)
pairs(Auto)
pairs(~ mpg + displacement + horsepower + weight + acceleration, Auto)
plot(horsepower, mpg)
identify(horsepower, mpg, name)
summary(Auto)
summary(mpg)